Inferential Statistical Methods Research Articles

Effect of Critical Thinking Skills on Misconceptions of Students in Learning Mathematics at the Elementary School Level

The aim of mathematics education in schools is to create logical reasoning and critical thinking ability in students. These skills development not only help students understand better but can also help remove the misconceptions they have about mathematical concepts. The purpose of this study was to investigate how critical thinking skills can help address misconceptions in mathematics learning at the elementary level. The research used an A-B-A design to determine the effects of an intervention, which is a perfect design for this purpose. 35 eighth-grade students who were enrolled in the current academic year were participants in this study. Three researcher-developed tools facilitated data collection. The first and initial tool was called the Test of Critical Thinking Skills (ToCTS), which measured students' critical thinking math skills. The second tool, the Test of Misconceptions (ToM), was developed to identify specifically how the students thought they were solving the problems. Finally, an interview protocol was used to learn about the students' reasoning regarding their answers on the tests. The mathematics curriculum of grades three to eight formed the basis of the development of these instruments, both close-ended and open-ended questions. Descriptive and inferential statistical methods were used in data analysis, and qualitative content from the interviews was applied to thematic analysis. A poor level of critical thinking skills in factoring equations and selecting proper solving methods for linear equations was shown by students. In addition, students had difficulty solving complete mathematical problems with linear equations. A significant gap was identified in their understanding and application of the BODMAS rule during algebraic operations. Based on these findings, the study recommended fostering critical thinking skills to improve students' abilities in factoring equations. Teachers should provide clear and comprehensive guidance to help students solve mathematical problems effectively. To address misconceptions, educators could introduce simplified problem-solving methods and reinforce students’ understanding of fundamental operations and rules. Specifically, it is essential to provide targeted instruction on the application of the BODMAS rule in algebraic contexts to enhance students' problem-solving capabilities.

Statistical Inference of Uncertain Autoregressive Model via the Principle of Least Squares

In the study of uncertain autoregressive models, how to estimate the unknown parameters and uncertain disturbance term in the models is always a key problem. In view of this, this paper proposes a statistical inference method based on the principle of least squares to determine the unknown parameters and uncertain disturbance term in an uncertain autoregressive model, and designs a numerical algorithm to calculate the numerical solutions of the corresponding estimators. Then, the uncertain hypothesis test is used to verify the applicability of the estimated uncertain autoregressive model, and point forecast and interval forecast are also made for the time series of future moments. Finally, a case study of the Consumer Price Index for all items in U.S. cities is provided to illustrate the effectiveness of the approach proposed in this paper.

Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.

Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks. This paper aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data; (2) describing the methods applicable to generalized linear models (GLM) and assessing their underlying distributional assumptions; (3) adapting existing methods to make them fully usable in health settings. A scoping review methodology was employed for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in health settings. Statistical theory was used to adapt methods and to derive the properties of the resulting estimators. From the review, 41 articles were selected, and six approaches were extracted for conducting standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information-sharing requirements and operational complexity. This paper contributes to the field of health analytics by providing an overview of the methods that can be used with horizontally partitioned data, by adapting these methods to the context of heterogeneous health data and by clarifying the workflows and quantities exchanged by the methods discussed. Further analysis of the confidentiality preserved by these methods is needed to fully understand the risk associated with the sharing of summary statistics.

THE INFLUENCE OF EMOTIONAL INTELLIGENCE, CULTURAL DIVERSITY IN ORGANIZATION, AND COMMUNICATION STRATEGIES ON LEADERSHIP EFFECTIVENESS IN DIVERSE WORKPLACES IN FUZHOU CITY, FUJIAN PROVINCE, CHINA

In today’s rapidly evolving business landscape, characterized by heightened complexity and relentless change, the demand for adept management is more pronounced than ever. As organizations grapple with the challenges presented by an increasingly interconnected and technologically advanced world, the need to adapt and evolve management practices becomes a paramount concern (Drejer et al., 2014; BAGU et al., 2011). The aims of this study were to investigate the factors influencing Leadership Effectiveness based on 4 aspects, namely, the Demographic Factors, the Emotional Intelligence, the Cultural Diversity in Organization, and the Communication Strategies. The quantitative method was applied. Descriptive statistics such as frequency, percent frequency, mean, and standard deviation are introduced. Various inferential statistical methods are used to test the hypothesis, particularly the Independent samples t-test, the One-way ANOVA, and the Multiple linear regression analysis. The results obtained from the study indicate that differences in Marital Status, Age, Educational Level, and Monthly Income generate differences in Leadership Effectiveness. Based on the Multiple Linear Regression analysis, all aspects of Emotional Intelligence (Self-awareness, Self-regulation, Motivation, Empathy, and Social Skills), Cultural Diversity in Organization (Diversity, Equity, Inclusion, and Belonging) and Communication Strategies (Awareness and Understanding, Communication Quality, Accessibility, and Employee Engagement) had a positive impact on Leadership Effectiveness.

Implementing traceability technologies to promote ethical sourcing in Ghana’s fashion and apparel industry

ABSTRACT The demand for traceability has emerged as a significant imperative within the framework of an ethical supply chain that includes multiple stages. This study focusses on the implementation and impact of traceability technology in the Ghanaian fashion industry. The study employed descriptive and explanatory research, using a 700-stakeholder sample, a standardised questionnaire, and descriptive and inferential statistical methods. The findings of the study indicate that QR codes, GPS tracking, sustainable material tracing, and consumer interaction apps demonstrate a high level of effectiveness within the Ghanaian fashion business. The implementation of traceability technology faces challenges like limited knowledge, research insufficient, lack of coordination, policy absence, financial constraints, technological infrastructure limitations, data privacy concerns, opposition, and training gaps. Stakeholders generally support the implementation of coordination policies and regulatory compliance strategies, suggesting that factors like awareness, research, cost, infrastructure, data protection, opposition, and training should be prioritised.

Neural Methods for Amortized Inference

Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.

Impact of the COVID 19 pandemic on Private Higher Education Institutions in Brazil

This research aimed to understand the transformations that occurred in private higher education institutions in Brazil during the pandemic period and the perceived impacts after this period. During the research, a bibliographic search on the subject in question was conducted, and an investigation instrument was applied to 114 educational managers from higher education institutions from all regions of Brazil. This instrument was developed with closed, multiple-choice, direct, and indirect questions and divided into two parts. The first part collected general data to understand the profile of the respondents, and the second part collected specific data on the topic relevant to this research. Descriptive and inferential statistical methods were applied to analyze the data. Qualitative variables were presented by absolute and relative frequency distributions. Quantitative variables were presented by measures of central tendency and variation. The results indicated that there were transformations in areas of the organization such as academic, financial, and planning and management, but the academic area was the one that underwent the most transformations overall. Another point highlighted was the type of academic organization, with University Centers being the most affected by strategic transformations. It was also noted that institutions were impacted in a similar way when compared by region. Finally, when comparing institutions based on the number of enrolled students, smaller institutions were the ones that underwent the most strategic transformations.

Key Factors Influencing Leadership Effectiveness in Diverse Workplaces in Fuzhou City, Fujian Province, China

he cultural and economic landscape of Fuzhou City in Fujian Province, China, is rich and diverse. Management effectiveness and emotional intelligence in diverse workplaces in Fuzhou City, Fujian Province, China, have been the subject of related research. The significance of emotional intelligence in fostering a productive workplace is a hot topic, as it allows individuals to recognize, understand, and manage their own emotions and those of others (Kargeti, 2023). Additionally, leadership effectiveness is influenced by emotional intelligence dimensions such as self-awareness, self-regulation, motivation, empathy, and social skills (Tian, 2022). Therefore, this study aims to investigate the factors influencing Leadership Effectiveness based on 5 aspects: Demographic Factors, Emotional Intelligence, Cultural Diversity in Organization, Communication Strategies, and Leadership Styles. The quantitative method is applied. Descriptive statistics such as frequency, percent frequency, the arithmetic mean, and the standard deviation are used to analyze the data collected from questionnaires. Suitable Inferential Statistical Methods are used to test the hypothesis, particularly the Independent Samples t-test, the One-way ANOVA, and the Multiple linear Regression Analysis. The results obtained from the study indicate that differences in Marital Status, Age, Educational Level, Monthly Income, and Working Experiences generate differences in Leadership Effectiveness. Based on the Multiple Linear Regression Analysis, all aspects of Emotional Intelligence (Self-awareness, Self-regulation, Motivation, Empathy, and Social Skills), Cultural Diversity in Organization (Diversity, Equity, Inclusion, and Belonging), Communication Strategies (Awareness and Understanding, Communication Quality, Accessibility, and Employee Engagement), and Leadership Styles (Decision-making Leadership Styles, Everyday Leadership Styles, Beyond Everyday-work Leadership Styles, Values-based Leadership Styles) are found out to have a positive impact on Leadership Effectiveness.

Social-Economic Factors and Secondary School Dropout among Girls in Mariani Ward, Tharaka-Nithi County, Kenya

This study investigated the high dropout rates among girls in secondary schools in Mariani Ward, a critical issue observed in many developing regions. The specific objectives of the study were to assess how household income, parental education, and family structure influence girls’ dropout rates. The study was grounded in the Relative Deprivation theory and utilized a descriptive survey design, involving a sample of 385 respondents selected from a larger population of 10,202 through cluster sampling method to ensure representation of various groups within the population. Within the target population, five clusters or sub-locations were identified based on their diverse characteristics. The sample included female dropouts as well as key informants such as head teachers, household heads, and local leaders. Data collection was achieved through questionnaires and interviews, and quantitative analysis was performed using SPSS version 25. The study employed inferential statistical methods, including correlation analysis, chi-square tests, and binomial logistic regression, to assess the impact of socio-economic factors on dropout rates. The results revealed significant positive correlations between dropout rates and household income (0.684), parental education status (0.547), and family structure (0.761). Binomial logistic regression analysis further confirmed the substantial influence of these factors on dropout rates. In addition to these factors, the study also indicated the presence of other unidentified variables affecting dropout rates. This highlights the necessity for further research to explore these additional influences. Based on these findings, the study recommends implementing targeted interventions to address the identified socio-economic challenges. These recommendations include developing financial support programs for low-income families, enhancing parental education and involvement, and fostering supportive family environments. Such measures are essential for reducing dropout rates and promoting sustained educational participation among girls in Mariani Ward. The study provides actionable insights for educators, policymakers, and stakeholders to effectively improve girls' access to education and support their academic persistence.

Modeling of Long-term Afterglow Counterparts to Gravitational Wave Events: The Full View of GRB 170817A

The arrival of gravitational wave astronomy and a growing number of time-domain-focused observatories are set to lead to an increasing number of detections of short gamma-ray bursts (GRBs) launched with a moderate inclination to Earth. Being nearby events, these are also prime candidates for very long-term follow-up campaigns and very long-baseline interferometry, which has implications for multi-messenger modeling, data analysis, and statistical inference methods applied to these sources. Here, we present a comprehensive modeling update that directly incorporates into afterglowpy astrometric observations of the GRB position, Poissonian statistics for faint sources, and modeling of a trans-relativistic population of electrons. We use the revolutionary event GW170817 to demonstrate the impact of these extensions both for the best-fit physics parameters and model selection methods that assess the statistical significance of additional late-time emission components. By including in our analysis the latest Chandra X-ray observations of GRB 170817A, we find only weak evidence (≲2σ) for a new emission component at late times, which makes for a slightly more natural fit to the centroid evolution and prediction for the external medium density.

The Study of Segregation and its Consequences in the Kabul City

This study investigates urban segregation in Kabul, the capital of Afghanistan, utilizing a multifaceted theoretical framework that integrates various explanatory models of segregation. The research targets the general population of Kabul, with a sample size of 233 individuals selected through a non-probabilistic cluster sampling technique. Data were collected using a comprehensive questionnaire focused on the Afghan context, incorporating insights from relevant studies. The analysis was conducted using SPSS software, employing both descriptive and inferential statistical methods. Demographic characteristics and participant responses in ethnically dominated areas were summarized using frequencies and percentages, while key variables were examined using measures of central tendency, dispersion, and distribution, including mean, standard deviation, variance, skewness, and kurtosis. The segregation tendencies of Kabul residents were analyzed using T-test, ANOVA and Spearman correlation. The results reveal a pronounced level of segregation within Kabul, with significant implications including the reinforcement of ethnocentric attitudes, the preservation of distinct subcultural identities, enhanced feelings of community security, and increased intra-group political and social engagement. The study found that followers of Shia Islam tend to segregate themselves from other religious groups, while the Pashtun ethnic group shows the lowest tendency toward segregation. Additionally, Kabul citizens tend to live among their own religious groups and the lower the level of education of individuals, the greater their tendency to separation.

Effects of Rainfall Variability on Maize Production in Migori County, Kenya

Maize is the main staple food crop in Kenya and is of vital concern to agricultural policy decisions, food security, and the overall development of the sector and the economy. It is also the dominant staple food crop in Migori-County. However, there has been a declining trend in maize production among farmers in the study area threatening household food security. This study was conducted in Migori County using cross sectional survey research design. A sample size of 384 households was selected through stratified and systematic sampling techniques though just 310 households accepted to be interviewed. Data was collected using a structured questionnaire. Validated secondary weather data for the period 2011-2023 was obtained from the Migori Meteorological Station. The collected data was entered into SPSS software, version 20, and analysed using both descriptive and inferential statistical methods. The results indicated that 60% of households have an adequate food supply, while 40% face food shortages. Further, there was a strong positive and significant relationship between maize production (yield) and annual weather conditions from 2018 to 2023 (r = 0.845, p = 0.05). This indicates that changes in weather patterns, such as rainfall and temperature, had a significant influence on maize yields during the study period. Hence, farmers should be encouraged to use income from cash crops to purchase food stocks and engage in non-farm activities as alternative income sources to mitigate the effects of rainfall variability and climate change on food security.

Assessment of Kidney Function Discrepancies in Pediatric CAKUT Patients Using Bedside Schwartz Equation and Renal Scintigraphy.

This research explores the correlation between estimated glomerular filtration rates (eGFR) obtained using the bedside Schwartz equation, and renal scintigraphy in children with congenital kidney and urinary tract abnormalities (CAKUT). The objective is to enhance understanding and management of renal health in this demographic by analyzing kidney size-function relationships. A retrospective observational analysis was performed on 94 pediatric CAKUT patients at the "Louis Turcanu" Emergency Hospital for Children, Timisoara. Kidney function data, extracted from medical records, were evaluated using the Schwartz equation, renal scintigraphy, and the gold standard iohexol clearance. Ethical approval was secured for the study, which employed descriptive and inferential statistical methods, including t-tests and correlation coefficients, to compare eGFR values. Significant variances were found in eGFRs across different body surface area (BSA) percentiles. For instance, the eGFR for the right kidney in the 25th-50th BSA percentile (102.02 ± 41.52 mL/min/BSA) was notably higher than that of the left (35.60 ± 26.05 mL/min/BSA; p = 0.01). The overall sample reflected a higher eGFR in the right kidney (76.03 ± 40.91 mL/min/BSA) compared to the left (57.46 ± 35.91 mL/min/BSA; p = 0.02). Additionally, a strong positive Pearson correlation (r = 0.80, p = 0.02) was found between scintigraphy and ultrasound measures in the 50th-75th percentiles for left renal percentiles, demonstrating consistent patterns across different evaluations of kidney function. This comparison indicates a complex relationship between eGFR values and kidney size, suggesting potential inaccuracies in standard bedside eGFR measurements for pediatric CAKUT patients. The findings underscore the necessity for accurate diagnostic tools specifically designed for pediatric applications and advocate for the integration of multiple diagnostic techniques to improve clinical management.

ASSEMBLER EDU E-MODULES: IMPROVING ARGUMENTATION SKILLS, PERSEVERANCE, AND CURIOSITY IN PHYSICS LEARNING

This study examined the impact of Assembler Edu-based e-modules on enhancing students' argumentation skills, perseverance, and curiosity in high school physics learning. Employing a quantitative ex-post facto design, the research utilized a 4-point Likert scale questionnaire to gather data. Descriptive and inferential statistical methods, including simple linear regression, were used for analysis. The findings reveal that using Assembler Edu-based e-modules significantly positively affected students' argumentation skills, with a 42.2% influence. Moreover, student responses indicated a 47.6% improvement in perseverance, while curiosity in learning renewable energy topics increased by 47.4%. These results highlight the considerable potential of integrating digital tools like Assembler Edu into the learning environment. This study introduces an innovative approach to physics education by incorporating interactive e-modules based on the Assembler Edu platform. Unlike traditional learning materials, these e-modules foster a more dynamic and engaging learning experience, enhancing students' critical thinking, perseverance, and curiosity. The research adds a fresh dimension to the existing literature by demonstrating how digital technology in science education can significantly improve core skills necessary for 21st-century learners. The study's implications suggest that integrating Assembler Edu-based e-modules into the school curriculum can enrich students' learning experiences, promote a deeper understanding of complex topics like renewable energy, and cultivate essential skills such as argumentation and perseverance, which are vital for success in both academic and real-world contexts.

Youth Participation in Agripreneurship in Trans Nzoia County, Kenya

The study focused on youth participation in agripreneurship in Trans Nzoia County, Kenya. The county being a bread basket of Kenya, the youth are a sign of progress in the agribusiness sector in the county and country at large. The study was guided by the following objectives; to assess effect of youth attitude on participation in Agripreneurship in Trans Nzoia County, Kenya, and to establish the effect of perceived benefits on youth participation in Agripreneurship in Trans Nzoia County, Kenya. The research theories in the study included; the pull- push theory, and unity maximization theory. A descriptive research design was adopted by the study. A target population of 10,080 respondents from which, a sample of 385 respondents was used for data collection and eventual analysis. Research questionnaires were used in collecting data. Both inferential and descriptive statistical methods were used for data analysis and presentation. This enabled the establishment of the strength and nature of relationship among the variables. The findings were expected to benefit county governments, national governments, youth, and various development agencies and to enrich the existing literature on youth programs and empowerment. The study employed the F-test in predicting the dependent variable which indicated that the model significantly predicted how the independent variables affected the dependent variable (youth participation in Agripreneurship in Trans Nzoia County, Kenya). The study further made recommendations on each of the variables for management consideration in future decision making processes. Keywords: Youth Participation, Youth Attitude, Perceived Benefits

Advancing health coaching: A comparative study of large language model and health coaches

ObjectiveRecent advances in large language models (LLM) offer opportunities to automate health coaching. With zero-shot learning ability, LLMs could revolutionize health coaching by providing better accessibility, scalability, and customization. The aim of this study is to compare the quality of responses to clients' sleep-related questions provided by health coaches and an LLM. Design, setting, and participantsFrom a de-identified dataset of coaching conversations from a pilot randomized controlled trial, we extracted 100 question-answer pairs comprising client questions and corresponding health coach responses. These questions were entered into a retrieval-augmented generation (RAG)-enabled open-source LLM (LLaMa-2-7b-chat) to generate LLM responses. Out of 100 question-answer pairs, 90 were taken out and assigned to three groups of evaluators: experts, lay-users, and GPT-4. Each group conducted two evaluation tasks: (Task 1) a single-response quality assessment spanning five criteria—accuracy, readability, helpfulness, empathy, and likelihood of harm—rated on a five-point Likert scale, and (Task 2) a pairwise comparison to choose the superior response between pairs. A suite of inferential statistical methods, including the paired and independent sample t-tests, Pearson correlation, and chi-square tests, were utilized to answer the study objective. Recognizing potential biases in human judgment, the remaining 10 question-answer pairs were used to assess inter-evaluator reliability among the human evaluators, quantified using the interclass correlation coefficient and percentage agreement metrics. ResultsUpon exclusion of incomplete data, the analysis included 178 single-response evaluations (Task 1) and 83 pairwise comparisons (Task 2). Expert and GPT-4 assessments revealed no discernible disparities in health coach and LLM responses across the five metrics. Contrarily, lay-users deemed LLM responses significantly more helpful than that of human coaches (p < 0.05). LLM responses were preferred in the majority (62.25 %, n = 155) of the aggregate 249 assessments, with all three evaluator groups favoring LLM over health coach inputs. While GPT-4 rated both health coach and LLM responses significantly higher than experts in terms of readability, helpfulness, and empathy, its ratings on accuracy and likelihood of harm aligned with those of experts. Response length positively correlated with accuracy and empathy scores, but negatively affected readability across all evaluator groups. Expert and lay-user evaluators demonstrated moderate to high inter-evaluator reliability. ConclusionOur study showed encouraging findings by demonstrating that RAG-enabled LLM has comparable performance to health coaches in the domain tested. Serving as an initial step towards the creation of more sophisticated, adaptive, round-the-clock automated health coaching systems, our findings call for more extensive evaluation which could assist in the development of the model that could in the future lead to potential clinical implementation.

Stablecoins for Transferring Value in the Cryptocurrency Market

Stablecoins have gained prominence as an effective means to mitigate the inherent volatility in the cryptocurrency markets by providing a stable and reliable medium for value preservation and transfer. This research paper investigates the attributes that render stablecoins a preferable vehicle for value retention and transaction within these markets. By employing a quantitative methodology, this study collected data through surveys, with a sample size of 714 individuals chosen via purposive sampling techniques. To analyze the data, both descriptive and inferential statistical methods were applied. The demographic profiles of the sample were summarized using descriptive statistics such as frequencies and percentages. For examining the relationships between demographic factors (such as gender, age, education, income, and place of residence) and the perceived efficacy of stablecoins, inferential techniques including Chi-square tests and logistic regression were utilized. The logistic regression analysis specifically assessed how these variables influenced the likelihood of participants favoring stablecoins for financial transactions. The results indicate that the preference for stablecoins as a means of holding or transferring value within the cryptocurrency domain in Thailand varies significantly across different demographic groups, specifically influenced by factors such as gender, education, income, and place of residence. These outcomes provide insightful implications concerning the demographic influences on the adoption and utilization of stablecoins in financial activities. Keywords: stablecoin, holding, transferring, value, cryptocurrency market

Examining Bhutanese Science Teachers’ Epistemic Views of Scientific Inquiry

AbstractScientific inquiry is regarded as the bedrock of science education in Bhutan. Bhutanese science teachers, for example, are increasingly required to possess accurate and deep epistemic views of scientific inquiry. Hence, this cross-sectional study was carried out to examine Bhutanese science teachers’ epistemic views of scientific inquiry. The study recruited 301 science teachers using convenience and snowball sampling procedures. Data was collected using Views About Scientific Inquiry (VASI) questionnaire administered through an online survey mode and analysed using descriptive statistics and inferential statistical methods. Findings from this study revealed Bhutanese science teachers as being ignorant of epistemic aspects related to questions and hypotheses, procedures of investigations, results, and procedures of investigations, scientific data and scientific evidence, and scientific explanations and scientific theories. The independent sample t-test revealed no significant difference between Bhutanese male and female science teachers’ epistemic views of scientific inquiry (p &gt; .05). The one-way ANOVA revealed significant differences amongst Bhutanese science teachers’ epistemic views of scientific inquiry based on academic qualification (p &lt; .05). The Tukey HSD post hoc test, however, showed the differences existing only between science teachers with master’s degree and certificate qualification in favour of the former (p &lt; .05). The three-way ANOVA revealed Bhutanese science teachers’ epistemic views of scientific inquiry as being independent of individual and interaction effects of school type, teaching subject, and teaching experience (p &gt; .05).

Empowering Women in Saudi Workforce: HR, Job Satisfaction, and Policies for Work–Life Balance

The present study investigates the sustainable influence of human resources (HR) support on job satisfaction and work–life balance among female employees in Saudi Arabia, underlining its significance in advancing social sustainability within the workforce. It seeks to analyze the role of job satisfaction as a mediator and organizational policies as moderators in these relationships. This study analyzed data from a simple random sample of 145 employed women in different sectors in Saudi Arabia, utilizing exploratory and confirmatory factor analyses, as well as inferential statistical methods for hypotheses testing. We conducted an evaluation of mediating and moderating effects in the investigated relationships using the PROCESS macro for SPSS. The findings show a strong positive relationship between HR support, work–life balance, and job satisfaction in Saudi Arabia’s female workforce. The mediation function of job satisfaction in the relationship between HR support and work–life balance emphasizes the critical role of positive working experiences in achieving work–life balance. Furthermore, this research highlights organizational policies as a crucial mediator, indicating that implementing supportive and progressive policies might amplify the beneficial impacts of HR practices on work–life balance and job satisfaction. The present study enhances the existing body of knowledge by offering empirical findings on the importance of culturally customized sustainable HR practices and the crucial function of organizational policies in advancing gender equality and achieving a healthy work–life balance.

Aerosol loading in the guinea coast climate region of Nigeria: comparison of MODIS and AERONET data sources

This study compares aerosol optical depth (AOD) measurements from satellite data with that from a ground-based station. It aims at validating monthly AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) collection 6.1 (Aqua and Terra) at 550 nm against AOD data from the Aerosol Robotic Network (AERONET) station at 500 nm in Ilorin over the period 2003–2022. The analysis focuses on eight selected cities/locations within the Guinea Coast region of Nigeria. Descriptive and inferential statistical methods, including correlation, regression, standard deviation, coefficient of variation, the Mann–Kendall trend test, root mean square error (RMSE), mean absolute error (MAE), and relative mean bias (RMB), were employed. The results indicate a positive correlation in most locations, with correlation values ranging from 0.3 to 0.7. The correlation between the datasets in Ilorin showed a positive and significant relationship while the other seven locations presented a weak and insignificant relationship. Additionally, the period from 2013–2022 showed better correlation compared to 2003–2012. AOD levels were highest during the harmattan season (DJF), exceeding 0.8, and lowest during the monsoon (JJA) and post-monsoon (SON) seasons, with values below 0.5, reflecting the effects of wet scavenging. The highest agreement between MODIS Aqua and AERONET data was observed during the harmattan season. The study also found that MODIS tends to overestimate AOD compared to AERONET and that MODIS-AOD exhibits greater spread and variability than AERONET-AOD.